Object Detection, Location and Identification at Radio Frequencies in the Near Field

最近在英国发生的事件（例如2019年伦敦大桥袭击事件，其中2人死亡，以及与恐怖有关的Streatham事件，其中2人被刺伤）强调了改进早期对峙威胁检测的必要性，其中包括刀，枪和简易爆炸装置。为了能够使用电磁场测量在距离传感器10米左右的距离处探测和识别这些小物体，需要在300 MHz至12 GHz范围内的频率，其中波传播效应很重要。这个范围内的频率传统上也用于雷达（无线电探测和测距），用于距离传感器远得多的距离上的大型物体（例如船舶，飞机和空中威胁）。然而，虽然雷达传统上与远场物体的定位和检测相关，但雷达也可用于近场物体的分类（例如自动驾驶汽车，停车传感器和用于寻找地雷和未爆弹药的探地雷达（GPR），考古搜索和建筑行业的公用事业位置）。此外，考虑到Google、Tesla、Uber和许多其他公司开发的自动驾驶汽车以及自动制造中的相关应用，人们对改进物体定位也有相当大的兴趣。在所有这些应用中，还存在相当大的需求，以改善不受电磁场可以穿透的边界（例如墙壁、地面、衣服、烟、雾或云）阻碍的小物体的表征和识别。该提案旨在使用300 MHz至12 GHz范围内的电磁频率改进近场小物体的表征、分类和识别，从而产生新的数学结果、物体识别统计计算工具和电磁传感器的设计建议。我们的假设是，一个更高的张量描述的对象结合概率分类方法提供了一种有效的手段，识别小物体使用电磁场测量定位远离目标，但在近场，在波传播频率。为了检验我们的假设，我们将推导出新的渐近展开式，这将导致新的张量描述方面的新的对象特征。我们将使用这些张量研究对象的新的最小压缩表示，并了解可以从这些最小表示中获得的对象信息。我们将开发新的计算工具来计算这些特征和分类器，这些特征和分类器建立在张量系数库的基础上，可以从实际测量中进行对象预测。

Recent events in the UK (eg the 2019 London Bridge Attack, in which 2 people were killed, and the Terror Related Streatham Incident, where 2 peoplewere stabbed) have highlighted the need for improved early stand-off detection of threats, which include knives, guns and improvised explosive devices. To be able to characterise and identify these small objects at stand-off distances in the order of 10s metres from the sensor using electromagnetic field measurements requires frequencies in the 300MHz to 12GHz range, where wave propagation effects are important. Frequencies in this range have also been traditionally been used in radar (radio detection and ranging) for large objects (eg ships, aircraft and air borne threats) over much larger distances from the sensor using far field scattering pattens. However, while radar is traditionally associated with the positioning and detection of objects in the far field, radar can also used be for the classification of objects in the near field (such as in autonomous vehicles, parking sensors, and ground penetrating radar (GPR) for finding landmines and unexploded ordnance, archaeological searches and the location of utilities for the construction industry). Furthermore, there is also considerable interest in improved object positioning given the development of autonomous vehicles by Google, Tesla, Uber and many others as well as related applications in autonomous manufacturing. In all these applications there is also considerable demand to improve the characterisation and identification of small objects that are not impeded by boundaries that can be penetrated by electromagnetic fields (eg walls, ground, clothing, smoke, fog or clouds). This proposal is aimed at improving the characterisation, classification and identification of small objects in the near field using electromagnetic frequencies in the range 300MHz to 12GHz leading to new mathematical results, statistical computing tools for object identification and design recommendations for electromagnetic sensors. Our hypothesis is that a higher tensor description of an object combined with a probabilistic classification approach provides an effective means of identifying small objects using electromagnetic field measurements positioned away from the target, but in the near field, at wave propagation frequencies. To test our hypothesis, we will derive new asymptotic expansions, which lead to new object characterisations in terms of new tensor descriptions. We will investigate new minimal contracted representations of objects using these tensors and understand the information about an object that can be obtained from these minimal representations. We will develop new computational tools for computing these characterisations and classifiers that build on a library of tensor coefficients to make object predictions from practical measurements.